# Staff Machine Learning Engineer, Traffic Intelligence

**Company**: Airbnb
**Location**: United States
**Work arrangement**: remote
**Experience**: staff
**Job type**: full-time
**Salary**: $212,000-$265,000 USD
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q63327

**Apply**: https://job-boards.greenhouse.io/airbnb/jobs/8129371?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_84ca8da5-8b4

## Description

We are seeking a Staff Machine Learning Engineer to join our Traffic Intelligence team. You will play a critical role in architecting and maintaining Airbnb's end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines.

**The Difference You Will Make:** Your primary objective will be to harden edge-traffic policies, targeting reduced bot-incident Mean Time To Mitigation (MTTM), and establish rigorous evaluation practices to ensure foundational signal accuracy and evasion-resistance across the fleet.

**A Typical Day:**

- Own the complete lifecycle of traffic-scoring models, from problem framing to real-time deployment, managing the adversarial feedback loop to ensure high evasion-resistance and directly drive reductions in bot-incident MTTM.

- Architect robust offline-to-online pipelines that produce certified source-of-truth datasets, establishing rigorous evaluation frameworks to ensure every model improvement is empirically measurable and defensible.

- Execute model optimization within strict millisecond latency budgets at the internet edge, uniquely balancing inference costs against incremental value while maintaining fleet-wide fail-open behaviors.

- Partner daily with security analysts, data platform engineers, and international infrastructure partners to integrate scoring intelligence into automated mitigation workflows.

- Serve as the team's machine learning authority, communicating complex model trade-offs to leadership and cross-functional teams.

**Your Expertise:**

- 9+ years of applied experience in production ML, specifically within non-stationary, adversarial domains (e.g., traffic integrity, bot mitigation, or fraud).

- Demonstrated experience architecting scalable, offline-to-online data pipelines that produce certified source-of-truth datasets for low-latency inference systems.

- Strong foundation in rigorous model evaluation, including metrics like ROC/AUC, precision/recall, and calibration.

- Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams.

- Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior, HTTP/TLS signatures).

- Proven track record of cross-functional leadership, landing initiatives through shared datasets and consumer contracts while mentoring junior engineers.

**Preferred Qualifications:**

- PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline.

- Advanced expertise in graph-based coordination or Sybil network detection methods.

- Deep experience with causal or econometric methods to model the business impact of false positives on legitimate user traffic.

- Experience implementing Bayesian calibration techniques for handling adversarially-biased, sparse, or imbalanced datasets.

**Our Commitment To Inclusion & Belonging:** Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions.

**How We'll Take Care of You:** The base pay range for this role is $212,000-$265,000 USD. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

## Skills

### Required
- production ML
- traffic integrity
- bot mitigation
- fraud
- scalable data pipelines
- low-latency inference systems
- rigorous model evaluation
- large-scale data engineering
- feature engineering
- internet edge infrastructure

### Nice to have
- graph-based coordination
- Sybil network detection methods
- causal or econometric methods
- Bayesian calibration techniques

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/airbnb/jobs/8129371?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)